Time series analysis plays a crucial rule in the telecom applications, such as network quality analysis, network capacity forecast, smart power management, etc. There’s a recent trend to apply machine learning methods (especially neural networks) to such problems, and they are reported to perform better in many cases than traditional methods such as autoregression and exponential smoothing.
However, building the machine learning applications for time series forecasting can be a laborious and knowledge-intensive process. In this talk, we present Project Zouwu, which provides Automated Machine Learning (AutoML) to time series analysis for Telco application. It is built on top of Ray (
https://github.com/ray-project/ray) and Analytics Zoo (
https://github.com/intel-analytics/analytics-zoo), so as to automate the process of feature generation and selection, model selection and hyper-parameter tuning in a distributed fashion. We will also share some real-world experience and “war stories” of earlier users.
